Low-Field Magnetic Resonance Image Enhancement using Undersampled k-Space
Authors: Daniel Tweneboah Anyimadu, Mohammed Abdalla, Mohammed M. Abdelsamea, Ahmed Karam Eldaly
Organizations: Department of Computer Science, University of Exeter, United Kingdom · Neurology Department, Royal Devon and Exeter Hospital, Exeter, United Kingdom · UCL Hawkes Institute, Department of Computer Science, University College London, London, United Kingdom
Low-field magnetic resonance imaging (MRI) offers a cost-effective alternative for medical imaging in resource-limited settings. However, its widespread adoption is hindered by two key challenges: prolonged scan times and reduced image quality. Accelerated acquisition can be achieved using k-space undersampling, while image enhancement traditionally relies on spatial-domain postprocessing. In this work, we propose a novel deep learning framework based on a U-Net variant that operates directly in k-space to super-resolve low-field MR images directly using undersampled data while quantifying the impact of reduced k-space sampling. Unlike conventional approaches that treat image super-resolution as a postprocessing step following image reconstruction from undersampled k-space, our unified model integrates both processes, leveraging k-space information to achieve superior image fidelity. Extensive experiments on synthetic and real low-field brain MRI datasets demonstrate that k-space-driven image super-resolution outperforms conventional spatial-domain counterparts. Furthermore, our results show that undersampled k-space reconstructions achieve comparable quality to full k-space acquisitions, enabling substantial scan-time acceleration without compromising diagnostic utility.
Figures & tables
Fig. 1 : Low-field versus high-field MR scans of the same subject. First column shows contrast change on axial plane, and middle and last columns show resolution and contrast change on coronal and sagittal planes. High-field images correspond to the 3T MRI from Human Connectome Project [ 37 ] ; and low-field images are simulated from the high-field images using a 0.3T MRI system.
Fig. 2 : kSURF: K-space dual channel U-Net for LF-MRI joint reconstruction, super-resolution/quality transfer and uncertainty quantification from undersampled k-space.
Fig. 3 : Images of sampling masks. (a) Cartesian sampling,(b) pseudo-radial sampling and (c) 2D variable density random sampling. Here, we show the 10% (top) and 50% (bottom) masks for Cartesian and Pseudo-radial sampling, and 5% (top) and 40% (bottom) masks of 2D-random sampling.
Table 4
Fig. 4 : Ground-truth high-field MR images used as representative examples for the InD and OOD test datasets.
Fig. 5 : Results using InD data. (A) reconstructions using LF/zero-filling, sIQT and kSURF across increasing pseudo-radial undersampling rates. (B) Corresponding error maps. (C) Corresponding uncertainty maps.
Fig. 6 : Results using OOD data. (A) reconstructions using LF/zero-filling, sIQT and kSURF across increasing pseudo-radial undersampling rates. (B) Corresponding error maps. (C) Corresponding uncertainty maps.
Methods
Metric
InD
OOD
LF/Zero-filling
SSIM
0.90 ± 0.01
0.84 ± 0.02
PSNR
29.95 ± 2.87
26.39 ± 3.01
LRTV
SSIM
0.85 ± 0.01
0.79 ± 0.02
PSNR
28.17 ± 2.87
24.60 ± 3.10
ESPCN
SSIM
0.92 ± 0.09
0.84 ± 0.03
PSNR
32.21 ± 4.33
27.10 ± 3.90
TABLE II : Comparison with other existing methods (in the spatial domain) using 100% sampling.
Fig. 7 : Results using real low-field data from healthy subjects. (A) reconstructions using LF/zero-filling, sIQT and kSURF across increasing pseudo-radial undersampling rates. (B) Corresponding uncertainty maps.
Fig. 8 : Results using real low-field data from unhealthy subjects. (A) reconstructions using LF/zero-filling, sIQT and kSURF across increasing pseudo-radial undersampling rates, with stroke pointed by a red arrow. (B) Corresponding uncertainty maps.
Low-field magnetic resonance imaging (MRI) provides affordable access to diagnostic imaging but suffers from prolonged acquisition and limited image quality. Accelerated imaging can be achieved with k-space undersampling, while super-resolution (SR) and image quality transfer (IQT) methods typically rely on spatial-domain post-processing. In this work, we propose a novel framework for reconstructing high-field MR like images directly from undersampled low-field k-space. Our approach employs a k-space dual channel U-Net that processes the real and imaginary components of undersampled k-space to restore missing frequency content. Experiments on low-field brain MRI demonstrate that our k-space-driven image enhancement consistently outperforms the counterpart spatial-domain method. Furthermore, reconstructions from undersampled k-space achieve image quality comparable to full k-space acquisitions. To the best of our knowledge, this is the first work that investigates low-field MRI SR/IQT directly from undersampled k-space.
Daniel Tweneboah Anyimadu, Mohammed M. Abdelsamea, Ahmed Karam Eldaly
Department of Computer Science, University of Exeter, Exeter, United Kingdom · UCL Hawkes Institute, University College London, London, United Kingdom
Low-field magnetic resonance imaging (MRI) offers affordable access to diagnostic imaging but faces challenges such as prolonged acquisition times and reduced image quality. Although accelerated imaging via k-space undersampling helps reduce scan time, image quality enhancement methods often rely on spatial-domain postprocessing. Deep learning achieved state-of-the-art results in both domains. However, most models are trained and evaluated using in-distribution (InD) data, creating a significant gap in understanding model performance when tested using out-of-distribution (OOD) data. To address these issues, we propose a novel framework that reconstructs high-field-like MR images directly from undersampled low-field MRI k-space, quantifies the impact of reduced sampling, and evaluates the generalisability of the model using OOD. Our approach utilises a k-space dual channel U-Net to jointly process the real and imaginary components of undersampled k-space, restoring missing frequency content, and incorporates an ensemble strategy to generate uncertainty maps. Experiments on low-field brain MRI demonstrate that our k-space-driven image quality enhancement outperforms the counterpart spatial-domain and other state-of-the-art baselines, achieving image quality comparable to full high-field k-space acquisitions using OOD data. To the best of our knowledge, this work is among the first to combine low-field MR image reconstruction, quality enhancement using undersampled k-space, and uncertainty quantification within a unified framework.
Daniel Tweneboah Anyimadu, Mohammed M. Abdelsamea, Ahmed Karam Eldaly
Department of Computer Science, University of Exeter, Exeter, United Kingdom · UCL Hawkes Institute, Department of Computer Science, University College London, London, United Kingdom
Fully sampled MRI requires dense k-space acquisition, leading to long scan times, reduced clinical throughput, and increased sensitivity to patient motion. Accelerated MRI addresses this by acquiring undersampled k-space data and reconstructing the missing information computationally. However, reconstruction from undersampled measurements is highly ill-posed and can introduce aliasing artifacts, noise amplification, and loss of anatomical detail. Although conventional parallel imaging and compressed sensing methods mitigate these issues, and deep learning methods have further improved reconstruction quality, preserving high-frequency structures under aggressive undersampling remains challenging. In this work, we propose a Variational Network with a Wavelet-based U-Net (W-UNet) for accelerated MRI reconstruction. The framework combines physics-guided iterative reconstruction with learnable multi-scale frequency representations. Standard pooling operations are replaced with Discrete Wavelet Transform and Inverse Wavelet Transform modules, enabling lossless downsampling while preserving low-frequency structure and high-frequency edge details. Integrated into the refinement and sensitivity map estimation stages, the proposed design improves artifact suppression, feature preservation, and reconstruction fidelity in both single-coil and multi-coil settings. Experiments on fastMRI knee and M4Raw brain datasets show state-of-the-art performance. Ablation studies further confirm the effectiveness of wavelet-based feature decomposition for accelerated MRI reconstruction.
Yasir Arafat Prodhan, Shaikh Anowarul Fattah
Department of Electrical and Electronic Engineering, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh · This paper was produced by the IEEE Publication Technology Group. They are in Piscataway, NJ.